NonBlocking StealHalf Work Queues


 Arline Stella Ryan
 2 years ago
 Views:
Transcription
1 NonBlocking StealHalf Work Queues Danny Hendler TelAviv University Nir Shavit TelAviv University Abstract The nonblocking workstealing algorithm of Arora et al. has been gaining popularity as the multiprocessor load balancing technology of choice in both Industry and Academia. At its core is an ingenious scheme for stealing a single item in a nonblocking manner from an array based deque. In recent years, several researchers have argued that stealing more than a single item at a time allows for increased stability, greater overall balance, and improved performance. This paper presents StealHalf, a new generalization of the Arora et al. algorithm, that allows processes, instead of stealing one, to steal up to half of the items in a given queue at a time. The new algorithm preserves the key properties of the Arora et al. algorithm: it is nonblocking, and it minimizes the number of CAS operations that the local process needs to perform. We provide analysis that proves that the new algorithm provides better load distribution: the expected load of any process throughout the execution is less than a constant away from the overall system average. 1 Introduction The workstealing algorithm of Arora et al. [2] has been gaining popularity as the multiprocessor loadbalancing technology of choice in both Industry and Academia [1, 2, 5, 7]. The scheme allows each process to maintain a local work queue, and steal an item from others if its queue becomes empty. At its core is an ingenious scheme for stealing an individual item in a nonblocking manner from a bounded size queue, minimizing the need for costly CAS (CompareandSwap) synchronization operations when fetching items locally. Though stealing one item has been shown sufficient to optimize computation along the "critical path" to within a constant factor [2, 4], several authors have argued that the scheme can be improved by allowing multiple items to be stolen at a time [3, 8, 9, 12]. Unfortunately, the only implementation algorithm for stealing multiple items at a time, This work was supported in part by a grant from Sun Microsysterns. Contact author: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the fifll citation on the first page. To copy otherwise, or repubfish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. PODC 2002, July 2124, 2002, Monterey, California, USA. Copyright 2002 ACM /02/ $5.00. due to Rudolph et al. [12], requires the local process owning the queue to use a strong synchronization operation (CAS or some other form of mutual exclusion) for every push or pop. Since the local process' operations are far more frequent, this makes the Rudolph et al. algorithm significantly less effective than that of Arora et al. This state of affairs leaves open the question of designing an algorithm that, like Arora et al., does not use costly synchronization operations in every step, yet allows stealing multiple items at a time, thus achieving the stability, balance, and improved performance of algorithms like Rudolph et al. 1.1 The New Algorithm This paper presents a new workstealing algorithm, Steal Half, a generalization of the Arora et al. algorithm, that allows processes to steal up to half of the items in a given queue at a time. Our StealHalf algorithm preserves the key properties of the Arora et al. algorithm: it is nonblocking and it minimizes the number of CAS operations that the local process needs to perform. We provide a performance analysis showing that our algorithm improves on Arora et al. by providing an overall system balance similar to the Rudolph et al. scheme: the expected load of any process throughout the execution is less than a constant away from the overall system average. In our algorithm, as in the Arora et al. algorithm, each process has a local work queue. This queue is actually a deque, allowing the local process to push and pop items from the bottom, while remote processes steal items from the top. The Arora et al. algorithm is based on a scheme that allows the local process, as long as there is more than one item in the deque, to modify the bottom counter without a CAS operation. Only when the top and bottom are a distance of 1 or less apart, so that processes potentially overlap on the single item that remains in the deque, does the process need to use a CAS operation to perform consensus. This consensus is necessary because the inherent uncertainty [6] regarding a single read or write operation to the top and bottom counters can affect whether the last item to be stolen is still there or not. This yields an algorithm where for any monotonic sequence of k pushes or k pops, 1 the number of 1A monotonic sequence is a sequence of local operations that monotonically either increases or decreases the bottom counter. We define the synchronization complexity in terms of monotonic sequences since one cannot make claims in nonmonotonic situations where the execution pattern of the underlying application causes thrashing back and forth on a single location. 280
2 CAS operations is O(1). In order to steal more than one item at a time, say half of the number of items in a deque, one must overcome a much greater uncertainty. Since a stealing process might remove up to half of the items, there are now many values of the bottom counter, say, up to half of the difference between top and bottom, for which there is a potential overlap. Thus, eliminating the uncertainty regarding reading and writing the counter requires consensus to be performed for half the items in the deque, an unacceptable solution from a performance point of view, since it would yield an algorithm with O(k) synchronization complexity. The key to our new algorithm is the observation that one can limit the uncertainty so that the local process needs to perform a consensus operation (use a CAS), only when the number of remaining items in the deque (as indicated by the difference between top and bottom), is a power of two! The scheme works so that as the distance between the bottom and top counters changes, it checks and possibly updates a special halfpoint counter. The uncertainty regarding a single read or write exists just as in the Arora et al. algorithm, only now it happens with respect to this counter. What we are able to show is that missing the counter update can only affect locations beyond the next poweroftwo at any given point. For any monotonic sequence of k pushes or k pops, our algorithm uses only O(logk) CAS operations, slightly worse than Arora et al., but exponentially better than the O(k) of the Rudolph et al. scheme. Like the Arora et al. scheme, our StealHalf algorithm is nonblocking: the slowdown of any process cannot prevent the progress of any other, allowing the system as a whole to make progress, independently of process speeds. Like Arora et al., the nonblocking property in our algorithm is not intended to guarantee faulttolerance: the failure of a process can cause loss of items. Arora et al. claim that their empirical testing on various benchmarks shows that the nonblocking property contributes to performance of work stealing, especially in multiprogrammed systems [2]. In our algorithm, the price for unsuccessful steal operations is a redundant copying of multiple itempointers, whereas in the Arora algorithm only a single pointer is copied redundantly. However in our algorithm, successful stems transfer multiple items at the cost of a single CAS, whereas in the Arora et al. scheme a CAS is necessary for each stolen item. 1.2 Performance Analysis Mitzenmacher [9] uses his differential equations approach [10] to analyze the behavior of work stealing algorithms in a dynamic setting, showing that in various situations stealing more than one item improves performance. Berenbrink et al. [3] have used a markov model to argue that a system that steals only one item at a time can slip into an instable state from which it cannot recover, allowing the number of items to grow indefinitely. This implies that no matter how much buffer space is allocated, at some point the system may overflow. Treating such overflow situations requires the use of costly overflow mechanisms [5]. Berenbrink et al. [3] further show that an Aroralike scheme that allows stealing, say, half of the items, will prevent that system from slipping into such an instable state. Rudolph et. al [12], and later Luling and Monien [8], prove that in a load balancing scheme that repeatedly balances the work evenly among random pairs of local work queues, the expected load of each process will vary only by a constant factor from the load of any other process and that the overall variance is small. The algorithm we present is generic, in the sense that one can change the steal initiation policy to implement a variety of schemes including those of [2, 3, 12]. We choose to analyze its behavior in detail under a steaiattempt policy similar to [12]: ever so often, every process p performs the following operation, which we call balancing initiation: p flips a biased coin to decide if it should attempt to balance its workload by stealing multiple items, where the probability of attempting is inversely proportional to its load. If the decision is to balance, then p randomly selects another process and attempts to balance load with it. Our main Theorem states that our algorithm maintains properties similar to those of Rudulph et al. We provide it here since the proof provided by Rudolph et al. turns out to be incomplete, and also because there are significant differences between the algorithms. As an example, the Rudolph et al. algorithm is symmetric: a process can both stealitemsfrom and insertitemsto the deque of another process. In our algorithm, however, a process can only steal items. Assume A~ is a time period where no deque grows or shrinks by more than u items (through pushing or popping). Let Lp,t denote the number of items in process p's deque at time t; also, let At denote the averageload at time t. Then if all processes perform balancing initiation every A~ time, there exists a constant c~, not depending on the number of processes or the application, such that: 'v'p,t : E[Lp,t] < oluat The existence of such Au is a natural assumption in most systems, and can be easily maintained with slight protocol modifications in others. In contrast, it can easily be shown, that if one steals a single item at a time as in the Arora et al. scheme, even if stealattempts are performed every A1, there are scenarios in which the system becomes instable. In summary, we provide the first algorithm for stealing multiple items using a single CAS operation, without requiring a CAS for every item pushed or popped locally. An outline of the algorithm is presented in Section 2; the analysis is presented in Section 3; finally, correctness claims are presented at Section 4. 2 The StealHalf algorithm As noted earlier, the key issue in designing an algorithm for stealing multiple items is the need to minimize the use of strong synchronization operations both when performing local operations and when stealing multiple items. A first attempt at an algorithm might be to simply let a process steal a range of items by repeatedly performing the original code of stealing a single item for every item in the group. This would make the algorithm a stealmany algorithm 2 but at a very high cost: to steal k items, the thief would have to perform k synchronization operations. The algorithm presented here, utilizes an extended deque data structure, a variant of the deque of [2] depicted in Figure 1, to achieve synchronization at a low cost. The extended deque differs from the deque of [2] in two ways: (1) it is implemented as a cyclic array, and (2) it contains a memberstructure, called steairange, which defines the range of items that can be stolen atomically by a thiefprocess. 2Obviously with this technique the itemsgroup is not stolen atomically. 281
3 Extended Deque deque stealrange 1 tag J *. ' up to 2^i items top I can be stolen atomically last j.w" * I' : ' there are at least 2"i bot * items outside the stealrange : the bottom of the deque is somewhere in this group of 2^0+1) items Figure 1: The extended deque The algorithm allows stealing about half the items of the victim in a single synchronization operation. It does this by always maintaining the invariant that the number of items in every process p's steairange is approximately half the number of total items in p's deque. Process p's steairange is updated by any process that succeeds in stealing items from p's deque, and also by p itself. To keep the number of synchronization operation performed by a local process p as low as possible, p updates its steairange when pushing or popping an item to/from its deque, only if at least one of the following events occur: The number of items in p's deque crosses a 2 i boundary, for some i E N; A successfulsteal operation from p's deque has occurred since the last time p modified its own steal Range. 2.1 Data structures Each process owns an extended deque structure, which it shares with all other processes. In this structure: deq is an array that stores pointers or handles to items, which are generated and consumed dynamically. It has DEQ_SIZE entries. The range of occupied entries in deq includes all the entries in the range: [top... bot)deq_size 3. As noted, it is implemented as a cyclic array. The steairange memberstructure extends the age structure from [2]. It contains the following fields: tag  as in [2], a time stamp on the updating of the steairange structure. A tag is required in order to overcome the ABA problem inherent to the CAS primitive. 4 As in [2], the bounded tag algorithm of [11] can be used. top  as in [2], the pointer to the topmost item in the deque. ~ 3[a  "  b)m denotes the halfopen entriesrange, from entry a upto entry b (including a but not b), modulus m. 4Assume a process p reads a value A from location m, and then performs a successful CAS operation, which modifies the contents of m. Process p's CAS should ideally succeed only if the contents of m was not modified since it read value A. The problem is, that if other processes modified m's value to value B and then back again to A in the interim  the CAS operation still succeeds. To overcome this problem, a tag field is added to the structure. 5Note, that because the extended deque is cyclic, the item pointed to by top is not necessarily the item stored at the topmost dequeentry. The item pointed at by top is invariably the first item of the stealrange. steailast  points to the last item to be stolen. The items that would be stolen next (unless the streal Range structure would be modified by the local process before the next steal) are in the range [top... steailast]d~q_~lze 6. The steallast variable can assume the special value null, which indicates there are no items to steal. The stealrange structure is modified via CAS operations. The bot field points to the entry following the last entry containing an item. Since the extended deque is cyclic, bot may be smaller than top. If bot and top are equal, the extended deque is empty. In addition to the shared extendeddeque structure, each process has a static and local structure, called prevsteairange, of the same type as steairange. It is used by the local process to determine whether a steal has occurred since the last time the process modified the stealrange. 2.2 Highlevel extendeddeque methods description We specify the algorithm in a generic manner, that allows "pluggingin", in a modular way, components that allow flexibility in regard to the conditions/policy that control when a stealattempt is initiated Balancing initiation code The code that appears in figure 2 should be performed by every process periodically, throughout the computation. IF (shouldbalanceo) Process *victim=randomprocesso; TryToSteal(victim); Figure 2: stealinitiation code The shouldbalance method determines the policy regarding when to initiate a steal attempt 7. Many policies are conceivable, a few of which are: Try to steal only when the local deque is empty: this is the scheme implemented by Arora et al. [2], and we call it: stealonempty. Try to steal probabilistically, with the probability decreasing as the number of items in the deque increases: this scheme was suggested by Rudolph et al. [12], and we call it probabilistie balancing s Try to steal whenever the number of items in the deque increases/decreases by a constant factor from the last time a stealattempt was performed: this is the policy suggested in [3]. 6[a b]m denotes the closed arrayrange, from entry a upto entry b (including both a and b), modulus m. 7This can be implemented as inlined code rather than as a method, if this code should be performed very often. SThe scheme, as described, is always probabilistic in the sense that the victim process is selected at random. The probabilistic balancing scheme adds yet another probabilistic factor. 282
4 Stealinitiation policies can vary significantly with respect to the extent they make the system balanced, but obviously none can guarantee that the system is balanced, if balancing is not attempted frequently enough. Consequently, we have to make some reasonable assumptions regarding the frequency at which the stealinitiation code is performed. Let Au be a timeperiod small enough, such that it is guaranteed that no process p changes its load by more than u items during that period, by generating/consuming items (thefts notwithstanding). In the analysis we prove, that if the probabilistie balancing policy is employed, and the stealinitiation code is performed once every Au period, then the expected number of dequeitems of any process p at any time during the execution is no more than o~ times the total average, where a~ is a constant that does not depend on the number of processes or the dynamic pattern of item generation/consumption 9. Not every stealinitiation policy can guarantee this property, though. It can easily be shown, that under the stealonempty policy, where only a single item is stolen at a time, a system can grow unbalanced beyond any bound, even if the stealinitiation code is performed every A1 time period! The balancing initiation code can be performed periodically by the system or by an application, or it can be implemented as a signalhandler pushbottom code A highlevel pseudocode of pushbottom is shown in Figure 3. RETIJRN_CODE pushbottom(item *e) { IF deq is full return DEQ_FULL deq[bot] = e increment hot in a cyclic manner IF (deq length now equals 2"i for some i) Try to CASupdate the stealrange to contain max(1,2"(i1)) items ELSE IF (stealrange!= prevstealrange) Try to CASupdate the stealrange to contain max(1,2"i) items, where the current length of the deque is in the range [ 2~(i+I),2~(i+2) ) IF CAS was performed successfully prevstealrange = stealrange Figure 3: pushbottom pseudo code The pushbottom operation is performed by the local process p, whenever it needs to insert a new item into its local deque. If the deque is not full, the new item is placed in the entry pointed at by bot, and bot is incremented in a cyclic manner. However, if following the insertion of the new item the length of p's deque becomes 2 i for some i > 0, then p tries to CASupdate its steairange to contain the topmost 2 i1 items 1. This is to make sure the length of the steal Range is not much less than half the total number of items in the deque 11 Process p has to update the steairange even if the dequelength is not a power of 2, if another process has succeeded 9Obviously, A~ itself does depend on this pattern. 1 or 1, if i=0. 1aWe actually prove in the full paper that for any process p, at any time, the length of p's stealrange is at least ~'th of the length of p's deque. Before pushbottom0 bot de( e ste~ o After pushbottomo dequ(!i' 4, 5, 6' y o bot. ~ 1514 Figure 4: The extended deque before and after a pushbottom. Right after pushbottom inserts a new item into the deque, it checks whether the deque length reaches a powerof2 boundary (in the above Figure, the 16'th item was added), in which case steairange is expanded to contain the first half of the dequeelements. in stealing items from p since the last time p updated its stealrange. Process p can identify this by comparing the current value of its steairange with the last value it wrote to it  which is stored at its local prevstealrange. If the values differ, p tries to set the length of its steairange (by a CAS) to be max(l, 2/), where 2 TM _< len(deq) < 2/+2. If p performed a successful CAS, prevstealrange is updated with the value it wrote. Figure 4 depicts the state of an extended deque before and after the 16'th item is pushed into it. Since subsequent to the push the deque contains a powerof2 number of items, pushbottom tries (and in this case succeeds) to expand stealrange. It is easily seen (and is proven in our analysis) that immediately after every successful CAS operation performed by pushbottom, assuming that the pushed item is not the first item in the deque 12, the following holds: 2 len(stealrange) < len(deq) < 4 len(steairange) popbottom code A highlevel pseudocode of popbottom is presented in Figure 5. The popbottom operation is performed by the local process p whenever it needs to consume another dequeitem. Section 1: In section 1 a process performs some prechecks to make sure the method may proceed. It first checks whether the deque is empty, in which case the method returns null; it next checks whether the length of the deque is 2 i for some i _> 0. If this is indeed the case, then the method tries to CASupdate its steal Range to contain the topmost 2 i2 items 13. This is done in order to maintain the invariant that the length of stealrange neve~ exceeds half the total number of 12If the pushed item is the first item of the deque, then the lengths of both the deque and the steairanye right after the CAS equal 1. lsor 1, if i <
5 Item *popbottomo { Section 1 IF deq is empty return null IF (deq length now equals 2"i for some i) Try to CASupdate the stealrange to contain max(l,2^(i2)) items ELSE IF (stealrange!= prevstealrange) Try to CASupdate the stealrange to contain max(l,2*i) items, where the current length of the deque is in the range (2^(i+1)... 2~(i+2) ] Section 2 IF CAS was performed successfully prevstealrange = stealrange ELSE IF a CAS was attempted but failed return ABORT; Before popbottom0 d~ue stealrange 4 o 4 vf.* 2: 1 ~=t / * 3, [tools * 4, I'"t 5, Ilasfl~ * 6, t. J ~ * 7 1 bot * 8 * 9 * l~ * 11 * 12 * 13 * 14 * After popbottomo deque s t e ~ a J bot Decrement bot in a cyclic manner Item *e = deq[bot] oldstealrange = this>stealrange IF oldstealrange does not contain bot return e (no need to synchronize) ELSE IF oldstealrange is empty { bot=o (the last item  e  was already stolen by another process) return null } ELSE { Try to CASupdate the stealrange to be empty IF succeeded return e (e was not stolen so far) ELSE return null (the last item  e  was already stolen by another process) } } Figure 5: popbottom pseudo code items in the deque, unless there's a single item in the deque. As in pushbottom, p has to CASupdate the stealrange even if the dequelength is not a power of 2, if another process has succeeded in stealing items from p since the last time p updated its stealrange. Process p can identify this by comparing the current value of its stealrange with the last value it wrote to it  which is stored at prevsteairange. If the values differ, p tries to set the length of its stealrange to be max(1,2i), where T +1 < len(deq) _< T +2. If popbottom performed a successful CAS, it updates prevsteairange with the value it wrote. If however a CAS was attempted and failed, popbottom returns a special value ABORT, indicating this situation. Note, that this can only happen if a successful steal has occurred concurrently with the method's execution. 14. Section 2: Section 2 is very similar to its stealone [2] counterpart: it pops the bottomitem e off the deque, and then reads steairange. If this read does not include e, then the method returns e and exits, as no 14An alternative implementation is to retry again and again in a loop, until the method succeeds in updating stealrange. Figure 6: The extended deque before and after a popbottom. Just before popbottom pops the bottommost item from the deque, it checks whether the deque length is exactly at a powerof2 boundary (in the above Figure, the 16'th item is about to be popped), in which case stealrange is being shrank to contain the first quarter of the dequeelements other process may have stolen e; otherwise, there are 2 possibilities:  stealrange is empty  e was stolen by another process during the method's execution. The method returns null.  e is the oneandonly item in the deque, in which case the method tries to CASupdate stealrange with an empty range value. If it succeeds  it returns e as its result, otherwise it returns null. Figure 6 depicts the state of an extended deque before and after the 16'th item is popped off it. Since prior to the pop the deque contains a powerof2 number of items, popbottom tries to shrink stealrange (and in this case succeeds). It is easily seen (and is proven in our analysis) that immediately after every successful CAS operation performed by popbottom the following inequalities hold 15 2 * len(steairange) _< len(deq) _< 4 * len(steairange) trytosteal code The try ToSteal method is the method that actually attempts to perform a steal. It is called by a local process p, after the stealinitiation policy has determined that a stealattempt should be made and a victim process has been selected. The pseudocode of trytosteal is shown in Figure 7. The method receives 2 parameters: d  a pointer to the victim process' extendeddeque, and plen  the number of items in p's deque, and returns the number of items actually stolen. It first reads d.stealrange and computes its length, 15The below inequalities hold, unless the number of items in the deque after the pop is 0 or 1. If the pop empties the deque, then the lengths of both the deque and the steal_range after the operation are 0; if a single item is left in the deque after the pop, the lengths are both
6 based on which the method can determine whether it is certain that the victim has more items than p has; if this is not the case  the method returns 0 without stealing any item. Otherwise, about half of the guaranteed difference between the sizes of the 2 deques is copied to p's deque, and then p tries to CASupdate the victim's stealrange. The length of the new steairange is set to be max(l, 2i2), where the length of the victim's deque before the theft was in the range: [2 i... 2i+1). As we prove in our analysis, this guarantees that the new steairange has length that is at most half, and at least one eighth the remaining number of items. If the CAS fails, the stealattempt has failed also, and the method returns 0; otherwise  the stealattempt has succeeded, and the method proceeds to update p's bot and steal Range to reflect the new number of items in the deque. Note the following: If another process q succeeds in stealing items from p's deque concurrently to p's successful stealattempt, then p might fail in updating its own steairange, but it would still manage to update its bot and complete the steal successfully. Although trytosteal can be performed within a signalhandler, it is required that no local operations (namely pushbottom or pop Bottom ) are performed concurrently with trytosteal's execution. unsigned int *trytosteal(exdeque d, int plen){ ) rangelen = length of d.oldstealrange oldlen = length of d.deque IF plen> 2*rangeLen 2 return 0 (no need to steal) numtosteal = rangelen  plen/2 Copy first numtosteal items to the bottom of local deq newsrlen = max(l, 2"(i2)) [where 2"i <= oldlen < 2"(i+i)] CASupdate d.stealrange to contain newsrlen items IF CAS was performed successfully { update local bot to insert stolen items to the deque newdeqlen = new number of items at the local deque newsrlen=max(l,2"i) [where 2"(i+2)> newdeqlen >=2"(i+i)] CASupdate local stealrange to contain newsrlen items IF CAS was performed successfully prevstealrange = stealrange return numtosteal; ) ELSE return 0; 3 Analysis Figure 7: trytosteal method pseudocode In our analysis, we investigate the properties of the StealHalf algorithm under the probabilistic balancing policy, aiming to show that under reasonable assumptions, it keeps the system balanced. The probabilistic balancing policy we employ is very similar to the one described in [12], with the following main differences: 1. In [12], a process initiating loadbalancing can either steal items from or insert items to a randomly selected process, whereas in our scheme the initiating process can only steal items. We therefore call the former scheme symmetric probabilistic balancing and the latter asymmetric probabilistic balancing. 2. The model presented in [12] is synchronous in the sense that it assumes that computation proceeds in timesteps and that all processes attempt loadbalancing in the beginning of every timestep, whereas in the asynchronous model we investigate this cannot be assumed. [12] supplies a proof that symmetric probabilistic balancing keeps the system well balanced. It turns out, however, that this proof is incomplete. In the analysis presented in this section, we therefore supply an alternative proof for the symmetric case and then extend it also for asymmetric probabilistic balancing and specifically for the SteaiHalf algorithm. For lack of space, some of the proofs are omitted. 3.1 Notation Wherever possible, we follow the notation of [12]. To simplify the analysis, we assume that an execution is composed of a series of timesteps. In the beginning of each timestep, all processes flip biased coins to determine whether or not they should attempt balancing (in other words, they perform the balancing initiation code), and according to the result attempt or do not attempt balancing. Let Lp,t denote the number of items in p's workpile in the beginning of step t; also, let At denote the average system workload in the beginning of step t, namely: At  ~pep Lp,~ [Pl Process p decides to perform a balancing attempt at time t with probability: _e_ Lp,t ' for some constant 1 > # > O. When Lp,t equals O, we define  Lp,t to be 1. Ifp does decide to balance at time t, then it randomly selects another process q to balance with and tries to communicate with q to that effect. We denote this event by select(p, q, t) and therefore we have: 1 p(s~t(p,x,t))= ~,  . Lp,t n If at time t any one of the processes p or q selects the other in a balancing attempt, we denote this event by: select(p, q, t), namely: select(p, q, t) de] ~ = select(p, q, t) V selec~(q,p, t). If at time t, there are a few processes that initiate a balancing attempt with process p, then only one of them gets selected randomly. If process q is the one, we say that q approaches p at time t and we denote this event by approaches(q,p,t). If process q is the only process initiating a balancing attempt with p at time t, then approaches(q,p,t) also holds. If at time t, approaches(q,p,t) and p does not initiate a balancing attempt at time t, then p and q balance at time t. We denote a balancing event between p and q at time t by: balance(p, q, t). If at time t, approaches(q,p,t) and approaches(p,q,t), then p and q balance at time t. If at time t, approaches(p,q,t) and approaches(q,r,t), where p r, then q decides between p and r with equal probability. We denote this event by decide(q,x,t). Finally, if at time t both decide(p,q,t) and decide(q,p,t) hold, then balance(p, q, t) holds. The changes in the workpile size of any process p at time step t come from two sources: items which are added 285
7   Lp,~ or consumed due to the code executed by p at that step, and items added as a result of balancing operations. Let contrib[p, t] denote the change in the size of p's workpile at timestep t which is a result of a balancing operation. In Section 3.2 we analyze symmetric probabilistic balancing. In Section 3.3 we analyze asymmetric probabilistic balancing in general, and the StealHalf algorithm in particular. 3.2 Symmetric probabilistic balancing analysis In the following we investigate the relationship between p's workpile size in the beginning of timestep t, and the expectance of contrib[p, t]. Lemma 3.1 For all p and t we have: P(select(p,x,t)) = 1  (1 # 1 1)(1 n  # 1 Lx,t n  1 )" Proof select(p, q, t) gel = select(p, ~ q, t) V select(q, p, t). Consequently we have: P(select(p, q, t)) = 1  P(~select(p, q, t)) = (1  P(~se~ect(p, q, t) )P(~se~ect(q,p, t) ) ). Finally note, that for all different pairs of processes x, y and for all t: tt 1 P(~select(x, y, t)) = 1  ~ * n  1" Q.E.D. Lemma 3.2 Let hi, 1 < i < n be n positive realnumbers, then the following holds: Proof ~ n ~ > n " i=1 ai  (2i=lai)/n We actually have to prove that16: ai i=1 j=l Note that for every two positive realnumbers a, b we have: By using inequality 1 we get: a aj b +  > 2. (1) a (~ hi) ~ =n+ aj 1 E (aj. q ~) > n+2* (~) = n 2. i=1 j=l l<i~j_<n Q.E.D. The following lemma states that P(select(p,q,t)) and P(balanee(p, q, t) differ only by a constant factor. 16This lemma is actually a rephrasing of the arithmetic/harmonic mean inequality. Since the proof is very short, we provide it for the sake of presentationcompleteness. Lemma 3.3 p and q, For every step t, and for every two processes 2~P(select(p, q, t)) < P(balance(p, q, t)) < P(select(p, q, t)). Proof outline: p and q can balance at step t only if at least one of them selects the other at that step. Consequently P(balance(p, q, t)) < P(select(p, q, t)). As for the other direction, note that if all the following conditions hold, it is guaranteed that balance(p, q, t) holds: C1: select(p,q, t) holds; C2: No other process r selects p or q at time t, namely: Vr ~ p, q : ~select(r, p, t) A ~select(r, q, t). C3: Neither of p, q select another process r at time t, or the following holds: p[q] selects q[p], q[p] selects a different process r, but q[p] decides to balance with p[q] rather than with r. In other words: (Vr # p, q : (~select(p, r, t) A select(q, r, t)) OR 3r : [(select(p, q, t) A select(q, r, t) A decide(q, p, t))] OR 3r : [select(q,p, t) A select(p, r, t) A decide(p, q, t)] Consequently we have: P(balance(p, q, t)) > P(select(p, q, t)) * P(C2) * P(C3). In the full proof we show that P(C2) ~ e~ and P(C3) > 1 and thus obtain the result. Theorem 3.4 Proof Let Lp,t = hat, a > 1, then: E[contrib(p, t)] = fl(a). Clearly  Lp,~ E[contrib(p,t)] = E P[balance(p,x,t)]L~'t 2 x6p,x~p (2) Let P~ denote the set of processes whose workpile size is larger than hat at the beginning of timestep t, and let P~ = P  P~+. We get: E[eontrib(p,t)] = ~cp+ P[balance(p,x,t)]L~'~Le,' + ~zepz P[balance(p, x, t)] n*'*~ Le't (3) Note, that the summation over P~+ contains only positive summands, whereas the summation over P~ contains only nonpositive summands. Consequently, we can use Lemma 3.3 (for each summation separately) to get: Lz,tLp,t E[contrib(p,t)] < ~&+P[seleet(p,x,t)] 2 + 2~ E~pj P[select(p, x, t)] L~,,~Lp,, (4) We now bound each sum separately from above. We start with the positive summands. By using Lemma 3.1 we get: ExeP~ P[seleet(p, x, t)] Lx"~ Lr't = 286
8 E~ep2.(l_(l_ t~ ~~y)(1 t~ ~_~_y))~_~_~_~2i = Lp,t L~,t By multiplying and rearranging we get: L~,t = E 2(ntt1) Lp,t x e P~ r xepa + p Lp,t 2(n 1) L~,t # E 2(n:~)2(~.t Lp,t )" We now bound A, B and C: A = ~ 2(~1)L~,~ ~cp2l='t <  2(n1)Lp,t ExeP L~,t = t~ nat  ~n 2(n1)aAt 2a(n 1) < 1. B is positive and so B is bound by 0 from above. As for C: #2 #2 C< 2(n_1)2( ~,<_/ < n " IP21 Combining the upper bounds for A, B and C we get:  Lp,t < 2. (5) E P[balance(p, x, t)] L~,t 2  xe Pa + Now, we bound the negative summands. First, note that IPa+l _< n a and so IP~I _> n(l  2)" Again, by multiplying and rearranging summands we get the same A, B and C components, and we bound them from above. A and C can be bounded by 1 and ~ respeen tively, in exactly the same way it was done for the positive summands; as for B: Lp,t _ #sat 1 B E 2(n p1) L=,t 2~i) E L~,t (6) zep~ zep~ I Noting that the average workpile length for processes in Pj is not more than At~ and using Lemma 3.2 we get: S _> n(1  ~) At By substituting this upper bound for S in Equation 6 we get: B_> (2(((((((((~_ "'#sat 1) ~(n(1 ~))_>_ 1 #(1~ ~)s = e(s). Combining the upper bounds on A, B and C, we get: Lx,t E P[balance(p,x, t)] 2 xepz Lp,t  a(s). (7) Finally, substituting the upper bounds of Equations 5 and 7 in Equation 4 concludes the proof of the theorem. Q.E.D. We now consider the effect of symmetric probabilistic balancing, when balancing attempts are performed at a certain frequency. We define the balancing quantum as the time duration of each timestep. If the balancing quantum is A, we say that the balancing frequency is ~. 1 We say that a loadbalancing algorithm with balancing quantum A is locallybounding, if there is a constant s, independent of the number of processes, such that for any execution the following is guaranteed: Vp, t : E[Lp,t] < sat. We also say that the workqueues system is slocally bounded at time t, if the following inequalities hold during at time t: Vp : Lp,t < sat. As noted earlier, the difference between Lp,t and Lp,t+A comes from two sources: from the effect of a balancing operation that may or may not take place at that timestep (and its contribution is denoted by contrib[p, t]), and from workitems that are generated and/or consumed by p in its application execution during that timestep. We denote by A~ a timequantum small enough, such that the application execution (balancing operations notwithstanding) does not change the length of any workpile by more than u items. The following theorem proves that symmetric probabilistic balancing with frequency 1 for any integer u, is a locally bounding scheme. Theorem 3.5 Assume symmetric probabilistic balancing is employed with balancingfrequency ~, 1 then there is a constant su, not depending on the number of processes or the application, such that if the system starts s~locally bounded  then the following inequalities hold: Vp, t : E[Lp,t] < swat. Proof According to Theorem 3.4, there is a constant c, not depending on the number of processes, such that: (/3 > 1) A (Lp, t >/3. At) ~ E[contrib(p, t)] < c/3. (8) We show that s~ = U 2.~ is the constant we are seeking. Note, that if L(p, t) > omat at the beginning of an execution quantum, then E[contrib(p, t)] < 2u. During a A~ timequantum of execution, Lp,t can grow by at most u items. During that period, the system average can decrease by at most u items, so during this period (Lp,t At) can grow by at most 2u items. Q.E.D. Corollary 3.6 Assume symmetric probabilistic balancing is employed with balancingfrequency ~,1 then there is a constant su, not depending on the number of processes or the application, such that if the system starts imbalanced and runs long enough  it eventually becomes s~locally bounded 3.3 StealHalf probabilistic balancing analysis The scheme described in [12] is symmetric in the sense that a balancing operation between two processes p, q can take place if either one of them initiates it. This is not the case 287
9 for the StealHalf algorithm since it only allows stealing items and does NOT support insertion of items In other words, if at time t, Lp,t < Lq,t, then only p can initiate a balancing operation at that time with q, and not viceversa. In the following we prove that asymmetric probabilistic balancing also possesses the nice property of being locally bounding. The following lemma states that P(select(p,q,t)) and P(balance(p,q, t) differ only by a constant factor also for asymmetric probabilistic balancing Lemma 3.7 For every time step t, and for every two processes p and q such that Lp,t <: Lq, t it holds that: 1 P(se~ect(p,q,t)) < P(balance(p,q,t)) < P(selee~(p,q,t)). 2e 2 The proof is almost identical to the proof of Lemma 3.3. Next, Theorem 3.8, corresponding to Theorem 3.4, is derived for the asymmetric case. Theorem 3.8 Let Lp4 = olaf, v~ > 1, then it holds for asymmetric probabilistic balancing that: E[contrib(p, t)] = ~(o~). Proof outline: Note, that Equation 4 holds also for asymmetric balancing; however, unlike the symmetric case, where we have to consider balancing initiation by all possible processpairs which include p  for asymmetric balancing, when we consider balancing operations at time t that affect p, we only have to consider the following events: 1. se~ect(p,x,t), for x E P2 2. seleel(x,p,t), for x E P~ The proof's structure is similar to the proof of Theorem 3.5 (the corresponding theorem for the symmetric case). Based on Theorem 3.8, the following theorem, corresponding to Theorem 3.5, is proven for the asymmetric case. The proof is almost identical. Theorem 3.9 Assume asymmetric probabilistic balancing is employed with balancingfrequency ~, 1 then there is a constant a,, not depending on the number of processes or the application, such that if the system starts atlocally bounded  then the following inequalities hold: Vp, t : E[Lp,t] < c~uat. The following theorem states that the StealHalf algorithm maintains the following invariant for all processes p at all times: at least one eighth the number of items in p's deque can be stolen atomically. Theorem 3.10 Under StealHalf load balancing (for all policies) the following holds: Vp, t : Len(steaIRange(p,t) ) > Len(deq(p, t) ) 8 The proof proceeds by enumerating the statements which potentially modify either Len(deq)) or Len(stealRange)) and showing that the invariant is maintained after each one of them is executed. It is rather technical and for lack of space is not provided here; still, let us explain an interesting scenario that is encountered in the course of the proof, that of consecutive successful steals from the same process. Before first steal d~ stea~ U ~." ~o, z>i im ~ 31 A,T i 63 pre~alrange ~ After first steal t IRag d kq~ eo s~ ne first : 32 steal : 41 bot prevstealrange : ~ second After second steal d ~..~ e stealrange : 0 hot : prevst~lrarlge : ~ 30 ~ 45 Figure 8: A scenario of 2 consecutive steals. In the first stealoperation, 14 items are stolen (out of the 32 items which can be stolen atomically). In the second steal, all of the 16 items in the stealrange are stolen. A x/sign in the prevstealrange box indicates that it's equal to the steal Range; an X sign indicates it is different from the stealrange Figure 3.3 shows a scenario where two consecutive stealoperations are performed on process p's deque, while in the meantime p is not performing any operation on its deque. Initially, p's stealrange contains 32 items, but the first thief only steals 14 of them (which is what it needs to balance with p). When the thief looks at bot, it equals 95, but before the stem's CAS operation completes, it may change within the range [ ]. To make sure that after the steal stealrange would not contain more than half the remaining items, the thief sets the new length of stealrange to be one eighth of the maximal possible value of bot plus 1, namely g 128 ~_ 16. In the second steal shown at Figure Figure 3.3, the thief steals all of the 16 items in the stealrange, and again sets the new length of stealrange to contain 2s ~s = 16 items. This is correct, since this time prevstealrange differs from stealrange, and so the local process p cannot change bot without performing a CAS. Remember that Theorem 3.9 above assumes synchrony, in the sense that processing proceeds in timesteps, and all processes perform balancinginitiation in the beginning of every timestep. Additionally, it assumes that up to half the items of any process can be stolen atomically. Contrary to this, the StealHalf algorithm's setting is entirely asynchronous, and though the balancing frequency of every process is guaranteed, processes perform their balancinginitiation operations in separate times; additionally, the Steal Half algorithm can only guarantee that at least one eighth of a process' items can be stolen atomically. The modifications required in the above proof to prove that StealHalf is a locally bounding scheme are straightforward. They are not brought here for lack of space, but are to appear in the full paper. 4 Correctness In the full paper we prove the following theorem which shows that our algorithm has the same nonblocking property as that of Arora et al: the collective progress of processes in accessing the extendeddeque structures is guaranteed. Theorem 4.1 The StealHalf algorithm on a collection of extendeddeques, with operations pushbottom, popbottom, and trytosteal, is nonblocking. 288
10 We note that both algorithms are not fault tolerant in the sense that process failures, though nonblocking, can cause the loss of items. In [13], Shavit and Touitou formally define the semantics of a pool data structure. A pool is an unordered queue, a concurrent data structure that allows each processor to perform sequences of push a pop operations with the usual semantics. In the full paper we provide the proofs of the key lemmata necessary to prove the following theorem: Theorem 4.2 The SteaIHalf algorithm on a collection of extendeddeques, with operations pushbottom, popbottom, and try ToSteal, is a correct implementation of a pool data structure. We define the complexity of our algorithm in terms of the total number of synchronization operations necessary by analyzing it for monotonic sequences of pushbottom and pop Bottom operations. We do so since one cannot make claims in situations where the execution pattern of the underlying application causes thrashing back and forth on a single deque entry. For a given deque, a monotonic sequence is one in which all operations are either pushbottom or popbottom but not both, with a possible interleaving of trytosteal operations. We prove the following: Theorem 4.3 For any monotonic sequence of length k by process p during which m successful steal attempts are performed on p's extended deque, p performs at most O(log(k)+ m) CAS operations on the extended deque. 5 Acknowledgments We would like to thank Dave Detlefs, Maurice Herlihy, Victor Luchangco, and Mark Moir for their helpful comments on earlier drafts of our algorithm. Though he denies it and has managed to conveniently lose all evidence, we believe Dave Detlefs came up with a stealhalf algorithm very similar to ours in the context of his work on Sun's Parallel Garbage Collection [5]. [5] FLOOD, C., DETLEFS, D., SHAVIT, N., AND ZHANG, C. Parallel garbage collection for shared memory multiprocessors. In Usenix Java Virtual Machine Research and Technology Symposium (JVM '01) (Monterey, CA, Apr. 2001). [6] HERLIHY, M. Waitfree synchronization. ACM Transactions On Programming Languages and Systems 13, 1 (Jan. 1991), [7] LEISERSON, AND PLAAT. Programming parallel applications in cilk. SINEWS: SIAM News 31 (1998). [8] LULING, R., AND MONIEN, B. A dynamic distributed load balancing algorithm with provable good performance. In ACM Symposium on Parallel Algorithms and Architectures (1993), pp [9] MITZENMACHER, M. Analysis of load stealing models based on differential equations. In A CM Symposium on Parallel Algorithms and Architectures (1998), pp [10] MITZENMACHER, M. The power of two choices in randomized load balancing. IEEE Transactions on Parallel and Distributed Systems 12, 10 (2001), [11] MoIa, M. Practical implementations of nonblocking synchronization primitives. In Proceedings of the 16th Annual ACM Symposium on Principles of Distributed Computing (August 1997), pp [12] RUDOLPH, L., SLIVKINALLALOUF, M., AND UPFAL, E. A simple load balancing scheme for task allocation in parallel machines. In A CM Symposium on Parallel Algorithms and Architectures (1991), pp [13] SHAVIT, N., AND TOUITOU, D. Elimination trees and the construction of pools and stacks. Theory of Computing Systems, 30 (1997), References [1] ACAR, U. A., BLELLOCH, G. E., AND BLUMOFE, R. D. The data locality of work stealing. In ACM Symposium on Parallel Algorithms and Architectures (2000), pp [2] ARORA, N. S., BLUMOFE, R. D., AND PLAXTON, C. G. Thread scheduling for multiprogrammed multiprocessors. Theory of Computing Systems 34, 2 (2001), [3] BERENBRINK, P., FRIEDETZKY, T., AND GOLDBERG, L. A. The natural workstealing algorithm is stable. In Proceedings of the 42th IEEE Symposium on Foundations of Computer Science (FOCS) (2001), pp [4] BLUMOFE, R., AND LEISERSON, C. Scheduling multithreaded computations by work stealing. In Proceedings of the 35th Annual Symposium on Foundations of Computer Science, Santa Fe, New Mexico. (November 1994), pp
Dynamic Load Balancing. Using WorkStealing 35.1 INTRODUCTION CHAPTER. Daniel Cederman and Philippas Tsigas
CHAPTER Dynamic Load Balancing 35 Using WorkStealing Daniel Cederman and Philippas Tsigas In this chapter, we present a methodology for efficient load balancing of computational problems that can be easily
More informationInformation Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay
Information Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay Lecture  17 ShannonFanoElias Coding and Introduction to Arithmetic Coding
More informationarxiv:1112.0829v1 [math.pr] 5 Dec 2011
How Not to Win a Million Dollars: A Counterexample to a Conjecture of L. Breiman Thomas P. Hayes arxiv:1112.0829v1 [math.pr] 5 Dec 2011 Abstract Consider a gambling game in which we are allowed to repeatedly
More informationBounded Cost Algorithms for Multivalued Consensus Using Binary Consensus Instances
Bounded Cost Algorithms for Multivalued Consensus Using Binary Consensus Instances Jialin Zhang Tsinghua University zhanggl02@mails.tsinghua.edu.cn Wei Chen Microsoft Research Asia weic@microsoft.com Abstract
More informationThe Goldberg Rao Algorithm for the Maximum Flow Problem
The Goldberg Rao Algorithm for the Maximum Flow Problem COS 528 class notes October 18, 2006 Scribe: Dávid Papp Main idea: use of the blocking flow paradigm to achieve essentially O(min{m 2/3, n 1/2 }
More information{emery,browne}@cs.utexas.edu ABSTRACT. Keywords scalable, load distribution, load balancing, work stealing
Scalable Load Distribution and Load Balancing for Dynamic Parallel Programs E. Berger and J. C. Browne Department of Computer Science University of Texas at Austin Austin, Texas 78701 USA 01512471{9734,9579}
More informationLoad Balancing with Migration Penalties
Load Balancing with Migration Penalties Vivek F Farias, Ciamac C Moallemi, and Balaji Prabhakar Electrical Engineering, Stanford University, Stanford, CA 9435, USA Emails: {vivekf,ciamac,balaji}@stanfordedu
More informationOn Dynamic Load Balancing on Graphics Processors
Graphics Hardware (28) David Luebke and John D. Owens (Editors) On Dynamic Load Balancing on Graphics Processors Daniel Cederman and Philippas Tsigas Department of Computer Science and Engineering Chalmers
More informationLoad Balancing. Load Balancing 1 / 24
Load Balancing Backtracking, branch & bound and alphabeta pruning: how to assign work to idle processes without much communication? Additionally for alphabeta pruning: implementing the youngbrotherswait
More informationA COOL AND PRACTICAL ALTERNATIVE TO TRADITIONAL HASH TABLES
A COOL AND PRACTICAL ALTERNATIVE TO TRADITIONAL HASH TABLES ULFAR ERLINGSSON, MARK MANASSE, FRANK MCSHERRY MICROSOFT RESEARCH SILICON VALLEY MOUNTAIN VIEW, CALIFORNIA, USA ABSTRACT Recent advances in the
More informationCharacterizations for Java Memory Behavior
Characterizations for Java Memory Behavior Alex Gontmakher Assaf Schuster Computer Science Department, Technion {gsasha,assaf}@cs.technion.ac.il Abstract We provide nonoperational characterizations of
More informationUniversal hashing. In other words, the probability of a collision for two different keys x and y given a hash function randomly chosen from H is 1/m.
Universal hashing No matter how we choose our hash function, it is always possible to devise a set of keys that will hash to the same slot, making the hash scheme perform poorly. To circumvent this, we
More informationTowards Relaxing STM. Christoph M. Kirsch, Michael Lippautz! University of Salzburg. EuroTM Workshop, April 2014
Towards Relaxing STM Christoph M. Kirsch, Michael Lippautz! University of Salzburg! EuroTM Workshop, April 2014 Problem linear scalability positive scalability good performance throughput (#transactions/time)
More informationComparison on Different Load Balancing Algorithms of Peer to Peer Networks
Comparison on Different Load Balancing Algorithms of Peer to Peer Networks K.N.Sirisha *, S.Bhagya Rekha M.Tech,Software Engineering Noble college of Engineering & Technology for Women Web Technologies
More informationObstructionFree Synchronization: DoubleEnded Queues as an Example
ObstructionFree Synchronization: DoubleEnded Queues as an Example Maurice Herlihy Computer Science Department Brown University, Box 1910 Providence, RI 02912 Victor Luchangco Mark Moir Sun Microsystems
More informationDynamic Load Balancing using Graphics Processors
I.J. Intelligent Systems and Applications, 2014, 05, 7075 Published Online April 2014 in MECS (http://www.mecspress.org/) DOI: 10.5815/ijisa.2014.05.07 Dynamic Load Balancing using Graphics Processors
More informationPIPELINING CHAPTER OBJECTIVES
CHAPTER 8 PIPELINING CHAPTER OBJECTIVES In this chapter you will learn about: Pipelining as a means for executing machine instructions concurrently Various hazards that cause performance degradation in
More informationOffline sorting buffers on Line
Offline sorting buffers on Line Rohit Khandekar 1 and Vinayaka Pandit 2 1 University of Waterloo, ON, Canada. email: rkhandekar@gmail.com 2 IBM India Research Lab, New Delhi. email: pvinayak@in.ibm.com
More informationLoad Balancing and Switch Scheduling
EE384Y Project Final Report Load Balancing and Switch Scheduling Xiangheng Liu Department of Electrical Engineering Stanford University, Stanford CA 94305 Email: liuxh@systems.stanford.edu Abstract Load
More informationGambling Systems and MultiplicationInvariant Measures
Gambling Systems and MultiplicationInvariant Measures by Jeffrey S. Rosenthal* and Peter O. Schwartz** (May 28, 997.. Introduction. This short paper describes a surprising connection between two previously
More informationOverview Motivating Examples Interleaving Model Semantics of Correctness Testing, Debugging, and Verification
Introduction Overview Motivating Examples Interleaving Model Semantics of Correctness Testing, Debugging, and Verification Advanced Topics in Software Engineering 1 Concurrent Programs Characterized by
More informationThe Trip Scheduling Problem
The Trip Scheduling Problem Claudia Archetti Department of Quantitative Methods, University of Brescia Contrada Santa Chiara 50, 25122 Brescia, Italy Martin Savelsbergh School of Industrial and Systems
More informationPredicting CPU Availability of a Multicore Processor Executing Concurrent Java Threads
Predicting CPU Availability of a Multicore Processor Executing Concurrent Java Threads Khondker Shajadul Hasan 1, Nicolas G. Grounds 2, John K. Antonio 1 1 School of Computer Science, University of Oklahoma,
More informationContributions to Gang Scheduling
CHAPTER 7 Contributions to Gang Scheduling In this Chapter, we present two techniques to improve Gang Scheduling policies by adopting the ideas of this Thesis. The first one, Performance Driven Gang Scheduling,
More informationBig Data & Scripting storage networks and distributed file systems
Big Data & Scripting storage networks and distributed file systems 1, 2, in the remainder we use networks of computing nodes to enable computations on even larger datasets for a computation, each node
More informationInterpreters and virtual machines. Interpreters. Interpreters. Why interpreters? Treebased interpreters. Textbased interpreters
Interpreters and virtual machines Michel Schinz 2007 03 23 Interpreters Interpreters Why interpreters? An interpreter is a program that executes another program, represented as some kind of datastructure.
More informationDr Markus Hagenbuchner markus@uow.edu.au CSCI319. Distributed Systems
Dr Markus Hagenbuchner markus@uow.edu.au CSCI319 Distributed Systems CSCI319 Chapter 8 Page: 1 of 61 Fault Tolerance Study objectives: Understand the role of fault tolerance in Distributed Systems. Know
More informationTransactional Support for SDN Control Planes "
Transactional Support for SDN Control Planes Petr Kuznetsov Telecom ParisTech WTTM, 2015 Software Defined Networking An emerging paradigm in computer network management Separate forwarding hardware (data
More informationEND TERM Examination, May 2014 (Model Test Paper) B.Tech (IT) IV Semester
END TERM Examination, May 2014 (Model Test Paper) B.Tech (IT) IV Semester Paper Code: ETCS  212 Subject: Operating Systems Time: 3 hrs Maximum Marks: 75 Note: Attempt five questions including Q. No. 1
More informationChapter 6, The Operating System Machine Level
Chapter 6, The Operating System Machine Level 6.1 Virtual Memory 6.2 Virtual I/O Instructions 6.3 Virtual Instructions For Parallel Processing 6.4 Example Operating Systems 6.5 Summary Virtual Memory General
More informationBrewer s Conjecture and the Feasibility of Consistent, Available, PartitionTolerant Web Services
Brewer s Conjecture and the Feasibility of Consistent, Available, PartitionTolerant Web Services Seth Gilbert Nancy Lynch Abstract When designing distributed web services, there are three properties that
More informationElemental functions: Writing dataparallel code in C/C++ using Intel Cilk Plus
Elemental functions: Writing dataparallel code in C/C++ using Intel Cilk Plus A simple C/C++ language extension construct for data parallel operations Robert Geva robert.geva@intel.com Introduction Intel
More informationOperatin g Systems: Internals and Design Principle s. Chapter 10 Multiprocessor and RealTime Scheduling Seventh Edition By William Stallings
Operatin g Systems: Internals and Design Principle s Chapter 10 Multiprocessor and RealTime Scheduling Seventh Edition By William Stallings Operating Systems: Internals and Design Principles Bear in mind,
More informationChapter 7D The Java Virtual Machine
This sub chapter discusses another architecture, that of the JVM (Java Virtual Machine). In general, a VM (Virtual Machine) is a hypothetical machine (implemented in either hardware or software) that directly
More informationOperating Systems. Virtual Memory
Operating Systems Virtual Memory Virtual Memory Topics. Memory Hierarchy. Why Virtual Memory. Virtual Memory Issues. Virtual Memory Solutions. Locality of Reference. Virtual Memory with Segmentation. Page
More informationThe Relation between Two Present Value Formulae
James Ciecka, Gary Skoog, and Gerald Martin. 009. The Relation between Two Present Value Formulae. Journal of Legal Economics 15(): pp. 6174. The Relation between Two Present Value Formulae James E. Ciecka,
More informationDistributed Systems: Concepts and Design
Distributed Systems: Concepts and Design Edition 3 By George Coulouris, Jean Dollimore and Tim Kindberg AddisonWesley, Pearson Education 2001. Chapter 2 Exercise Solutions 2.1 Describe and illustrate
More informationHow Useful Is Old Information?
6 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 11, NO. 1, JANUARY 2000 How Useful Is Old Information? Michael Mitzenmacher AbstractÐWe consider the problem of load balancing in dynamic distributed
More informationmax cx s.t. Ax c where the matrix A, cost vector c and right hand side b are given and x is a vector of variables. For this example we have x
Linear Programming Linear programming refers to problems stated as maximization or minimization of a linear function subject to constraints that are linear equalities and inequalities. Although the study
More informationCompetitive Analysis of On line Randomized Call Control in Cellular Networks
Competitive Analysis of On line Randomized Call Control in Cellular Networks Ioannis Caragiannis Christos Kaklamanis Evi Papaioannou Abstract In this paper we address an important communication issue arising
More informationEND TERM Examination(Model Test Paper) Fourth Semester[B.Tech]
END TERM Examination(Model Test Paper) Fourth Semester[B.Tech] Paper Code: ETCS  212 Subject: Operating System Time: 3 hrs Maximum Marks: 75 Note: Q.No.1 is compulsory. Attempt any four questions of remaining
More informationThe StudentProject Allocation Problem
The StudentProject Allocation Problem David J. Abraham, Robert W. Irving, and David F. Manlove Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK Email: {dabraham,rwi,davidm}@dcs.gla.ac.uk.
More informationLecture 4 Online and streaming algorithms for clustering
CSE 291: Geometric algorithms Spring 2013 Lecture 4 Online and streaming algorithms for clustering 4.1 Online kclustering To the extent that clustering takes place in the brain, it happens in an online
More informationVirtual Time and Timeout in ClientServer Networks
Virtual Time and Timeout in ClientServer Networks Jayadev Misra July 13, 2011 Contents 1 Introduction 2 1.1 Background.............................. 2 1.1.1 Causal Model of Virtual Time...............
More informationConditions for Strong Synchronization In Concurrent Data Types
Conditions for Strong Synchronization In Concurrent Data Types Maged Michael IBM TJ Watson Research Center Joint work with Martin Vechev and Vijay Sarsawat Joint work with Hagit Attiya, Rachid Guerraoui,
More informationStability and Efficiency of a Random Local Load Balancing Protocol
Stability and Efficiency of a Random Local Load Balancing Protocol Aris Anagnostopoulos Dept. of Computer Science Brown University Providence, RI 0292 aris@cs.brown.edu Adam Kirsch Dept. of Computer Science
More informationLow upper bound of ideals, coding into rich Π 0 1 classes
Low upper bound of ideals, coding into rich Π 0 1 classes Antonín Kučera the main part is a joint project with T. Slaman Charles University, Prague September 2007, Chicago The main result There is a low
More informationCost Model: Work, Span and Parallelism. 1 The RAM model for sequential computation:
CSE341T 08/31/2015 Lecture 3 Cost Model: Work, Span and Parallelism In this lecture, we will look at how one analyze a parallel program written using Cilk Plus. When we analyze the cost of an algorithm
More informationSEER PROBABILISTIC SCHEDULING FOR COMMODITY HARDWARE TRANSACTIONAL MEMORY. 27 th Symposium on Parallel Architectures and Algorithms
27 th Symposium on Parallel Architectures and Algorithms SEER PROBABILISTIC SCHEDULING FOR COMMODITY HARDWARE TRANSACTIONAL MEMORY Nuno Diegues, Paolo Romano and Stoyan Garbatov Seer: Scheduling for Commodity
More informationHow Asymmetry Helps Load Balancing
How Asymmetry Helps oad Balancing Berthold Vöcking nternational Computer Science nstitute Berkeley, CA 947041198 voecking@icsiberkeleyedu Abstract This paper deals with balls and bins processes related
More informationCOMP 250 Fall Mathematical induction Sept. 26, (n 1) + n = n + (n 1)
COMP 50 Fall 016 9  Mathematical induction Sept 6, 016 You will see many examples in this course and upcoming courses of algorithms for solving various problems It many cases, it will be obvious that
More informationFacing the Challenges for RealTime Software Development on MultiCores
Facing the Challenges for RealTime Software Development on MultiCores Dr. Fridtjof Siebert aicas GmbH HaidundNeuStr. 18 76131 Karlsruhe, Germany siebert@aicas.com Abstract Multicore systems introduce
More informationChapter 6 Concurrent Programming
Chapter 6 Concurrent Programming Outline 6.1 Introduction 6.2 Monitors 6.2.1 Condition Variables 6.2.2 Simple Resource Allocation with Monitors 6.2.3 Monitor Example: Circular Buffer 6.2.4 Monitor Example:
More informationHardware Assisted Virtualization
Hardware Assisted Virtualization G. Lettieri 21 Oct. 2015 1 Introduction In the hardwareassisted virtualization technique we try to execute the instructions of the target machine directly on the host
More informationNotes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand
Notes V General Equilibrium: Positive Theory In this lecture we go on considering a general equilibrium model of a private ownership economy. In contrast to the Notes IV, we focus on positive issues such
More informationICS Principles of Operating Systems
ICS 143  Principles of Operating Systems Lectures 15 and 16  Virtual Memory Prof. Ardalan Amiri Sani Prof. Nalini Venkatasubramanian ardalan@uci.edu nalini@ics.uci.edu Virtual Memory Background Demand
More informationFirewall Verification and Redundancy Checking are Equivalent
Firewall Verification and Redundancy Checking are Equivalent H. B. Acharya University of Texas at Austin acharya@cs.utexas.edu M. G. Gouda National Science Foundation University of Texas at Austin mgouda@nsf.gov
More informationAlok Gupta. Dmitry Zhdanov
RESEARCH ARTICLE GROWTH AND SUSTAINABILITY OF MANAGED SECURITY SERVICES NETWORKS: AN ECONOMIC PERSPECTIVE Alok Gupta Department of Information and Decision Sciences, Carlson School of Management, University
More informationKeywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age.
Volume 3, Issue 10, October 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Load Measurement
More informationIntroduction to Java A First Look
Introduction to Java A First Look Java is a second or third generation object language Integrates many of best features Smalltalk C++ Like Smalltalk Everything is an object Interpreted or just in time
More informationTwo General Methods to Reduce Delay and Change of Enumeration Algorithms
ISSN 13465597 NII Technical Report Two General Methods to Reduce Delay and Change of Enumeration Algorithms Takeaki Uno NII2003004E Apr.2003 Two General Methods to Reduce Delay and Change of Enumeration
More informationVictor Shoup Avi Rubin. fshoup,rubing@bellcore.com. Abstract
Session Key Distribution Using Smart Cards Victor Shoup Avi Rubin Bellcore, 445 South St., Morristown, NJ 07960 fshoup,rubing@bellcore.com Abstract In this paper, we investigate a method by which smart
More informationVersioned Transactional Shared Memory for the
Versioned Transactional Shared Memory for the FénixEDU Web Application Nuno Carvalho INESCID/IST nonius@gsd.inescid.pt João Cachopo INESCID/IST joao.cachopo@inescid.pt António Rito Silva INESCID/IST
More informationCPU Scheduling Yi Shi Fall 2015 Xi an Jiaotong University
CPU Scheduling Yi Shi Fall 2015 Xi an Jiaotong University Goals for Today CPU Schedulers Scheduling Algorithms Algorithm Evaluation Metrics Algorithm details Thread Scheduling MultipleProcessor Scheduling
More informationLecture 6 Online and streaming algorithms for clustering
CSE 291: Unsupervised learning Spring 2008 Lecture 6 Online and streaming algorithms for clustering 6.1 Online kclustering To the extent that clustering takes place in the brain, it happens in an online
More informationSCALABILITY AND AVAILABILITY
SCALABILITY AND AVAILABILITY Real Systems must be Scalable fast enough to handle the expected load and grow easily when the load grows Available available enough of the time Scalable Scaleup increase
More informationUniversity of Waterloo Midterm Examination
University of Waterloo Midterm Examination Spring, 2006 Student Name: Student ID Number: Section: Course Abbreviation and Number Course Title CS350 Operating Systems Sections 01 (14:30), 02 (11:30) Instructor
More informationIMPROVING PERFORMANCE OF RANDOMIZED SIGNATURE SORT USING HASHING AND BITWISE OPERATORS
Volume 2, No. 3, March 2011 Journal of Global Research in Computer Science RESEARCH PAPER Available Online at www.jgrcs.info IMPROVING PERFORMANCE OF RANDOMIZED SIGNATURE SORT USING HASHING AND BITWISE
More informationOn Sorting and Load Balancing on GPUs
On Sorting and Load Balancing on GPUs Daniel Cederman and Philippas Tsigas Distributed Computing and Systems Chalmers University of Technology SE42 Göteborg, Sweden {cederman,tsigas}@chalmers.se Abstract
More informationLoad Balancing Between Heterogenous Computing Clusters
Load Balancing Between Heterogenous Computing Clusters SiuCheung Chau Dept. of Physics and Computing, Wilfrid Laurier University, Waterloo, Ontario, Canada, N2L 3C5 email: schau@wlu.ca Ada WaiChee Fu
More informationNetwork (Tree) Topology Inference Based on Prüfer Sequence
Network (Tree) Topology Inference Based on Prüfer Sequence C. Vanniarajan and Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai 600036 vanniarajanc@hcl.in,
More informationDECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH
DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH P.Neelakantan Department of Computer Science & Engineering, SVCET, Chittoor pneelakantan@rediffmail.com ABSTRACT The grid
More informationDecentralized Utilitybased Sensor Network Design
Decentralized Utilitybased Sensor Network Design Narayanan Sadagopan and Bhaskar Krishnamachari University of Southern California, Los Angeles, CA 900890781, USA narayans@cs.usc.edu, bkrishna@usc.edu
More informationSignatureFree Asynchronous Binary Byzantine Consensus with t < n/3, O(n 2 ) Messages, and O(1) Expected Time
SignatureFree Asynchronous Binary Byzantine Consensus with t < n/3, O(n 2 ) Messages, and O(1) Expected Time Achour Mostéfaoui Hamouma Moumen Michel Raynal, LINA, Université de Nantes, 44322 Nantes Cedex,
More information2 Exercises and Solutions
2 Exercises and Solutions Most of the exercises below have solutions but you should try first to solve them. Each subsection with solutions is after the corresponding subsection with exercises. 2.1 Sorting
More informationThe Operating System Level
The Operating System Level Virtual Memory File systems Parallel processing Case studies Due 6/3: 2, 3, 18, 23 Like other levels we have studied, the OS level is built on top of the next lower layer. Like
More informationCHAPTER 9 Exercises 9.1 Answer: 9.2 Ready Running Blocked Answer: 9.3
CHAPTER 9 Virtual memory can be a very interesting subject since it has so many different aspects: page faults, managing the backing store, page replacement, frame allocation, thrashing, page size. The
More informationProcedia Computer Science 00 (2012) 1 21. Trieu Minh Nhut Le, Jinli Cao, and Zhen He. trieule@sgu.edu.vn, j.cao@latrobe.edu.au, z.he@latrobe.edu.
Procedia Computer Science 00 (2012) 1 21 Procedia Computer Science Topk best probability queries and semantics ranking properties on probabilistic databases Trieu Minh Nhut Le, Jinli Cao, and Zhen He
More informationConcurrent Data Structures
1 Concurrent Data Structures Mark Moir and Nir Shavit Sun Microsystems Laboratories 1.1 Designing Concurrent Data Structures............. 11 Performance Blocking Techniques Nonblocking Techniques Complexity
More informationCMSC 858T: Randomized Algorithms Spring 2003 Handout 8: The Local Lemma
CMSC 858T: Randomized Algorithms Spring 2003 Handout 8: The Local Lemma Please Note: The references at the end are given for extra reading if you are interested in exploring these ideas further. You are
More informationA New Natureinspired Algorithm for Load Balancing
A New Natureinspired Algorithm for Load Balancing Xiang Feng East China University of Science and Technology Shanghai, China 200237 Email: xfeng{@ecusteducn, @cshkuhk} Francis CM Lau The University of
More informationBuffer Manager. Buffer Management. 1 Functional Principles of the Database Buffer
Buffer Management The database buffer is the mediator between the basic file system and the tupleoriented file system. The buffer manager s task is to make the pages addressable in main memory and to
More informationReal Time Programming: Concepts
Real Time Programming: Concepts Radek Pelánek Plan at first we will study basic concepts related to real time programming then we will have a look at specific programming languages and study how they realize
More informationAn example of a computable
An example of a computable absolutely normal number Verónica Becher Santiago Figueira Abstract The first example of an absolutely normal number was given by Sierpinski in 96, twenty years before the concept
More informationThe ConTract Model. Helmut Wächter, Andreas Reuter. November 9, 1999
The ConTract Model Helmut Wächter, Andreas Reuter November 9, 1999 Overview In Ahmed K. Elmagarmid: Database Transaction Models for Advanced Applications First in Andreas Reuter: ConTracts: A Means for
More informationAn Improved Design of Optical LIFO Buffer with Switched Delay Lines
2011 IEEE 12th International Conference on High Performance Switching and Routing An Improved Design of Optical LIFO Buffer with Switched Delay Lines Xiaoliang Wang, Xiaohong Jiang and Achille Pattavina
More informationFrom Spontaneous Total Order to Uniform Total Order: different degrees of optimistic delivery
From Spontaneous Total Order to Uniform Total Order: different degrees of optimistic delivery Luís Rodrigues Universidade de Lisboa ler@di.fc.ul.pt José Mocito Universidade de Lisboa jmocito@lasige.di.fc.ul.pt
More informationConcepts of Concurrent Computation
Chair of Software Engineering Concepts of Concurrent Computation Bertrand Meyer Sebastian Nanz Lecture 3: Synchronization Algorithms Today's lecture In this lecture you will learn about: the mutual exclusion
More informationLoad Balancing in Distributed Data Base and Distributed Computing System
Load Balancing in Distributed Data Base and Distributed Computing System Lovely Arya Research Scholar Dravidian University KUPPAM, ANDHRA PRADESH Abstract With a distributed system, data can be located
More informationTesting LTL Formula Translation into Büchi Automata
Testing LTL Formula Translation into Büchi Automata Heikki Tauriainen and Keijo Heljanko Helsinki University of Technology, Laboratory for Theoretical Computer Science, P. O. Box 5400, FIN02015 HUT, Finland
More informationLOOKING FOR A GOOD TIME TO BET
LOOKING FOR A GOOD TIME TO BET LAURENT SERLET Abstract. Suppose that the cards of a well shuffled deck of cards are turned up one after another. At any timebut once only you may bet that the next card
More informationChapter 5 Linux Load Balancing Mechanisms
Chapter 5 Linux Load Balancing Mechanisms Load balancing mechanisms in multiprocessor systems have two compatible objectives. One is to prevent processors from being idle while others processors still
More informationCompact Representations and Approximations for Compuation in Games
Compact Representations and Approximations for Compuation in Games Kevin Swersky April 23, 2008 Abstract Compact representations have recently been developed as a way of both encoding the strategic interactions
More informationJob Reference Guide. SLAMD Distributed Load Generation Engine. Version 1.8.2
Job Reference Guide SLAMD Distributed Load Generation Engine Version 1.8.2 June 2004 Contents 1. Introduction...3 2. The Utility Jobs...4 3. The LDAP Search Jobs...11 4. The LDAP Authentication Jobs...22
More informationConvex analysis and profit/cost/support functions
CALIFORNIA INSTITUTE OF TECHNOLOGY Division of the Humanities and Social Sciences Convex analysis and profit/cost/support functions KC Border October 2004 Revised January 2009 Let A be a subset of R m
More informationHagit Attiya and Eshcar Hillel. Computer Science Department Technion
Hagit Attiya and Eshcar Hillel Computer Science Department Technion !!" What are highlyconcurrent data structures and why we care about them The concurrency of existing implementation techniques Two ideas
More informationExceptions in MIPS. know the exception mechanism in MIPS be able to write a simple exception handler for a MIPS machine
7 Objectives After completing this lab you will: know the exception mechanism in MIPS be able to write a simple exception handler for a MIPS machine Introduction Branches and jumps provide ways to change
More informationProblem Set #1 Solutions
CS161: Design and Analysis of Algorithms Summer 2004 Problem Set #1 Solutions General Notes Regrade Policy: If you believe an error has been made in the grading of your problem set, you may resubmit it
More information(Refer Slide Time: 01:1101:27)
Digital Signal Processing Prof. S. C. Dutta Roy Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture  6 Digital systems (contd.); inverse systems, stability, FIR and IIR,
More informationWeek 4: Gambler s ruin and bold play
Week 4: Gambler s ruin and bold play Random walk and Gambler s ruin. Imagine a walker moving along a line. At every unit of time, he makes a step left or right of exactly one of unit. So we can think that
More information